The Big Bang in Business Intelligence

Transcription

1 The Big Bang in Business Intelligence Dan Holle CTO, BI Solutions 2007 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice

2 We have 45 minutes to Challenge conventional wisdom about BI & DW Look at operational, realtime examples of the changing face of BI Consider how technology needs to change to meet new needs

3 So 42 minutes left Let s challenge conventional wisdom about DW and BI.

4 DW & BI are both mature technologies. BI and Data Warehousing have been around for decades. We re fine-tuning, doing incremental improvements and cost reductions. No Way! DW appliances Best-of-breed pure-play BI tools, BI suites, BI appliances Data integration still evolving: ETL, ELT Metadata management, MDM, data quality BI and BPM/CPM integration SOA Real-time BI, Operational BI, Pervasive BI Market consolidation

5 BI low fruit have already been picked Areas where BI provides value have already been implemented by most companies. No Way! Real-time customer contacts are 5-10x more effective than unsolicited (outbound) marketing at generating sales But less than 15% of DW/BI shops are operationalized 90% of executives say front-line decisions are critical to profitability But 80% say most of the decisions that can be automated are not

6 BI = Reporting Some BI shops crank out loads of reports or maybe they have a few smart guys who discover stuff using SQL. That s BI. No Way! Maybe LAST CENTURY or so, BI meant reporting. No more. The big benefits come when BI/DW is integrated into the core business operational BI. For example...

7 21 st Century BI: Real-World Examples

8 Innovative Users of BI Last Century s BI IT process is back-office, batch Business deliverable is Reporting Supporting existing business processes Let s look at some who are pushing the envelope Integrating into the mainstream business Going beyond Reporting New data-driven business processes

9 Con-Way Freight Competes with Fedex, UPS Lacking scale, they needed to do something different to compete Differentiation: Technology SOA: 1999 CEP: 2003 Owes its existence to real-time architecture Analytics deeply integrated into both strategic and tactical initiatives Growth to $4B in revenue Recognized in Fortune s 2007 list of Most Admired Companies #1 in Transport/Logistics

10 National Bank Old way: Acquisitions lead to conflicting customer & product data Product sales based on organizational inertia, not logic New way: Customer data brought together in coherent structure Product offers optimized based on customer risk profile Result 30% reduction in delinquencies (Source: Smart Enough Systems, Taylor & Raden)

11 Harrah s Gaming Properties include not just Harrah s, but also Caesar s, Bally s, Rio, etc Poster child for analytics CEO is a nerd Revenues exceed $10B Cognos, SAS, TIBCO Used analytics to move from 30% market share to 50% Revenue impact: $4B Deep analysis of customer behavior Cross-sell, pain point Real Time Birthdays and other events Recognizing bad days and good days as they happen Responding optimally

12 Yahoo! The old way: UI experts argue about how to organize the web site The new way: Real-time experimentation and analytics MEASURE which site organization is better Example: Where to place the search box? Ran an experiment with hundreds of thousands of tests (just a few hours) Answer: the center! Value to Yahoo! is an incremental $20M a year!

13 Disney Paris Innovator in Customer Analytics & Real Time 1995: Introduced BI (mostly reporting) 1998: Hotel capacity optimization 2004: Real time for shops, restaurants 2005: Real time for attractions Managing operations in real time Redeploy people based on demand, on 15 minute intervals Example: It rains at a property. People are moved based on changing customer distribution Adjustments based on both forecasts and actual

14 Capital One A pioneer in marketing analytics 30,000 experiments a year Over a million sales a year through Customer Service When a customer calls to close an account, They might be routed to an automated service that makes it easier. Or They might be routed to a specialist who knows an offer likely to retain that customer Source: Super Crunching, Ian Ayres, p. 47

15 BI challenge: right-time/operational use of extracted knowledge Retail Real-time inventory forecasting/monitoring by item by store Personalized offers at checkout E-retail Clickstream analysis to Predict preferences based on previous activity Deliver 1-to-1 marketing Automated problem resolution Multichannel marketing Government National security Interstate crime records available to police in real time The business intelligence domain Manufacturing Real-time trend analysis Scrap, defective components, production rate, supply chain visibility RFID Vendor performance Telecom CDR analysis To predict and reduce churn and fraud, and improve revenue management and network utilization Financial Branch automation Fraud Compliance Healthcare EMRs Clinical repository Online claims processing Pay for performance

16 Hewlett-Packard! Printer division, Vancouver Manufacturing facility, generating $280M a year in revenue MIT PhD student studied the operation, using analytical modeling Modified the flows and buffer capacities in the plant Doubled the capacity with minimal change to WIP inventory $280M incremental revenue per year! Thought: Given that BPM systems capture the task flows of an entire Enterprise, can we optimize the whole Enterprise the same way?

17 What have we learned? Business value is changing the BI/DW focus Yesterday: Fine-tuning existing business process by doing reporting across the Enterprise Tomorrow: Using evidence-based management to change the business process in real time Plugging into the mainstream business Benefits often north of $100M

18 Re-Defining Data Warehousing and Business Intelligence

19 The time-value of data for decisioning Business Value Business event Operational BI can reduce latency and time to action to increase business value Valu e Data latency Analysi s latency Decision latency Data ready for analysis Information delivered Action taken Action time Source: Richard D. Hackathorn, Bolder Technology, Inc. Time 19

20 Data warehouse evolution model Strategic, predictive Stage 1 Stage 2 REPORTING ANALYTICAL Stage 3 PREDICTIVE Strategic + tactical, post-predictive Stage 4 OPERATIONAL Stage 5 ADAPTIVE CHASM What Why did it What will What is What do I happened? happen? happen? happening? want to happen? Inactive Reactive Proactive Autonomous Custom scripts ETL + scripts EAI + ETL + scripts Transactional Data Mgmt BATCH RIGHT-TIME NEAR REAL TIME REAL TIME Weeks Days Hours Minutes Seconds Sub-second

21 Changing Face of Data Warehousing Strategic Reporting Stand-alone Batch updates Few users doing strategic analysis Fast response is nice Availability is important Operational Automating action Integrated with the business Continuous online updates Thousands of users performing many types of tasks Stringent real time SLA s Mission critical availability

22 Through 2010, mixed workload performance will remain the single most important performance issue in data warehousing. the transactional DBMSs may be able to erode the performance edge formerly attributed to specialized data warehouse DBMS solutions. Magic Quadrant for Data Warehouse Database Management Systems, 2007, Gartner, 10 October 2007, Donald Feinberg, Mark Beyer)

23 Observation: Unless the Data Warehouse platform is a solid performer in transaction processing, ANOTHER platform will be needed for operational delivery of analytics to the mainstream business. higher cost, more complexity, and most importantly, less agility.

24 Mission-critical requires system availability Data warehouses built for an analytics-only workload will be severely challenged by the rigors of a new workload that includes connected, mission-critical applications. The warehouse must be highly available and disaster recoverable. By 2010, > 90% of Global 2000 companies plan to incorporate analytics into multiple operational applications that access the DW. But fewer than 15% of DWs are designed to provide HA, failover, DR and remaining components of mission-critical systems Operational Analytics and the Emerging Data Warehouse, Gartner, 14 May 2007, Mark Beyer

25 Observation: Unless the Data Warehouse platform is capable of meeting operational availability requirements ANOTHER platform will be needed for operational delivery of analytics to the mainstream business. higher cost, more complexity, and most importantly, less agility.

26 Grumbling in paradise sound bites Last century s BI / DW paradigm batch, complex query is showing its age: Our EDW is really just a big back-office data mart. Creating insights is one thing; delivering them to the operational business is another. Pervasive BI isn t just delivering more reports to more people.

27 Somehow, somewhere, it seems we got lost. Data Warehousing stands separate from the core, operational business. How did that happen?

28 The Innovator s Dilemma A book by Clayton Christensen His observation: Many companies get trapped into incremental improvements of a successful product: Continuous Innovation This ultimately reaches a plateau (based on some success metric) This creates a circumstance where a new entrant can introduce Disruptive Innovation by following a different, more relevant success metric

29 Product Performance In the beginning: Operational Operational Databases Short transactions Each DB request very simple High availability requirements Stringent response time SLAs Time or Engineering Effort

30 Product Performance After that: BI Changed the metrics Big data, not big transaction volumes Complex operations OLTP could not handle Batch has inherent efficiencies Can t do OLTP, but that s okay Time or Engineering Effort

31 Product Performance Next Generation: Operational BI Best of both: World-class complex query World-class operational delivery World-class availability Commodity hardware Next Generation Data Warehouse Last Century s Data Warehouse Operational Databases Time or Engineering Effort

32 HP Neoview for Operational BI An integrated hardware and software platform for data warehousing launched April, 2007 Powerful enough for the most complex and demanding EDW projects Strong technology around high availability and realtime operational access Enterprise class Appliance-like simplicity Affordable Services

33 Neoview addresses next-generation BI Cost Performance Capability/ Availability Complexity Commodity, industry standard H/W Simplified operations and DB admin Reduces data mart proliferation Designed for scalability High performance and concurrency Optimized for mixed workloads No DBA maintenance downtimes Industry s leading 24x7x365 platform MPP architecture tailored for BI HP service portfolio expertise Preconfigured, integrated, and tested Proactive remote support from HP Solutions delivery capability

34 Neoview is new. HP s Presence in BI is not. >200,000 BI infrastructure sales per year; servers, storage, services 3,500+ employees working on BI products and services Funding in the 100s of millions of dollars

35 The HP partnership is part of a continued effort to drive innovation into every facet of Wal-Mart s business and IT operations. Nancy Stewart, Wal-Mart chief technology officer At Wal-Mart, we never underestimate the importance of investing in innovative solutions that will improve our ability to understand and anticipate our customers needs. Our experience with HP Neoview has proven that we made the right decision to partner with HP for our nextgeneration business intelligence needs. Jim Scantlin, Wal-mart director, enterprise information management

36 Conclusions Companies have come to realize that the biggest benefits of BI and analytics come from enterprise wide deployment. Seizing the BI Opportunity, May 2006 Business Week Research Services

37 The highest-value place For Enterprise Business Intelligence is in the mainstream, operational Business itself!

38 Time s Up! NO QUESTIONS!!!